docstr: Args:
      opt (Namespace): options
      scene (BaseScene): the background scene
      dset_type (str): train/val/test set
      split (str): split file path
docstr_tok: ["Args",":","opt","(","Namespace",")",":","options","scene","(","BaseScene",")",":","the","background","scene","dset_type","(","str",")",":","train\/val\/test","set","split","(","str",")",":","split","file","path"]
code: def __init__(self, opt, scene, dset_type, split):
      """
      Args:
          opt (Namespace): options
          scene (BaseScene): the background scene
          dset_type (str): train/val/test set
          split (str): split file path
      """
      self.opt = opt
      self.batch_size = opt.rays_per_batch
      self.scene = scene
      self.dset_type = dset_type
      self.split = split
      self.inclusions = neuman_helper.read_text(split)
      print(
          f"{dset_type} dataset has {len(self.inclusions)} samples: {self.inclusions}"
      )
  
file_path: ml-neuman/datasets/background_rays.py
imports: import numpy
  import torch
  from torch.utils import data
  from data_io import neuman_helper
  from utils import ray_utils
  from utils.constant import TRAIN_SET_LENGTH, VALIDATION_SET_LENGTH
label_desc: Use image node_id 1 to create a new BackgroundRayDataset object from inherited base classes: data with example: obj = BackgroundRayDataset(opt, scene, dset_type, split)